Predicción del valor a ofertar en una subasta de vehículos utilizando Machine Learning

The Florida-based used vehicle recycling company, ABA, obtains its raw material through its primary activity, which is buying used vehicle in auctions. The determination of the price to offer in the auctions is the most relevant problem for ABA, since , if the price is too low, it is likely that another buyer will win the auction, while if it is too high, the profits of the company are affected. In this project, Machine Learning techniques and advanced data analytics were used to predict the auction prices of these vehicles. Exploratory analyzes were carried out to evaluate the behavior of the supplied data and its correlation between variables. Different algorithms were compared, such as multiple linear regression, XG-Boost, classification and regression trees, and Random Forest. The models obtained were analyzed based on criteria such as the MAPE to identify the one that yielded the minimum error in the prediction of the values to be offered for the vehicle. With the proposed solution, savings of approximately 24% were achieved, compared to the method used by the company previously.

• Autores: Carmona, David; González, Marianella; Ruiz, Natalia
• Fecha de publicación: 2022
• Institución: Universidad del Norte
• Repositorio: Repositorio Uninorte
• Idioma: spa
• OAI Identifier: oai:manglar.uninorte.edu.co:10584/11549
• Acceso en línea: http://hdl.handle.net/10584/11549
• Palabra clave: Predicción; Árboles de regresión; Regresión Lineal Múltiple; Machine Learning; XG-Boost; Random Forest
• License: Universidad del Norte